This guide provides insights into defining, saving, and restoring TensorFlow models, emphasizing the foundational role of tf.Module. Models in TensorFlow, essential for machine learning, consist of layers that utilize trainable variables to compute outputs through a forward pass. Understanding TensorFlow’s architecture allows developers to efficiently build and manage their models. Properly saving and restoring these models ensures that computations and learned variables are retained, which is vital for continuous learning and deployment in various applications.
TensorFlow models consist of layers, which include trainable variables, and are built on the foundational class: tf.Module.
Models in TensorFlow perform a forward pass on tensors, and they encompass both functions and variables, which can be modified through training.
A model is defined through its structure with reusable functions, typically employing tf.Module and representing a core part of machine learning.
Saving and restoring models in TensorFlow ensures that the defined computations and variables are preserved for future use, aiding in efficient training and deployment.
Collection
[
|
...
]